Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations2129
Missing cells405
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory612.3 B

Variable types

Text2
DateTime3
Numeric17
Categorical4

Dataset

DescriptionVIDA 007 - COVID-19 vaccine trial
CreatorRP2 Clinical Data Harmonization Project
URLHEAT Research Projects

Variable descriptions

study_sourceSource study identifier
CD4 cell count (cells/µL)CD4+ T lymphocyte count - immune function indicator
HIV viral load (copies/mL)HIV RNA copies per mL - treatment efficacy marker
Albumin (g/dL)Serum albumin - liver function and nutritional status
primary_datePrimary date of measurement/visit
Age (at enrolment)Patient age at study enrollment

Alerts

systolic blood pressure is highly overall correlated with Diastolic blood pressure (mmHg) and 2 other fieldsHigh correlation
diastolic blood pressure is highly overall correlated with Diastolic blood pressure (mmHg) and 2 other fieldsHigh correlation
weight is highly overall correlated with BMI (kg/m²) and 1 other fieldsHigh correlation
Height is highly overall correlated with Height (m) and 1 other fieldsHigh correlation
oral temperature is highly overall correlated with HEAT_VULNERABILITY_SCORE and 1 other fieldsHigh correlation
BMI (kg/m²) is highly overall correlated with Weight (kg) and 1 other fieldsHigh correlation
Height (m) is highly overall correlated with Height and 1 other fieldsHigh correlation
Weight (kg) is highly overall correlated with BMI (kg/m²) and 1 other fieldsHigh correlation
Systolic blood pressure (mmHg) is highly overall correlated with Diastolic blood pressure (mmHg) and 2 other fieldsHigh correlation
Diastolic blood pressure (mmHg) is highly overall correlated with Systolic blood pressure (mmHg) and 2 other fieldsHigh correlation
Temperature (°C) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 1 other fieldsHigh correlation
month is highly overall correlated with seasonHigh correlation
season is highly overall correlated with month and 1 other fieldsHigh correlation
Sex is highly overall correlated with Height and 1 other fieldsHigh correlation
Race is highly overall correlated with original_record_indexHigh correlation
original_record_index is highly overall correlated with seasonHigh correlation
Respiratory rate (breaths/min) is highly overall correlated with original_record_indexHigh correlation
HEAT_VULNERABILITY_SCORE is highly overall correlated with Temperature (°C) and 1 other fieldsHigh correlation
HEAT_VULNERABILITY_SCORE is highly imbalanced (98.5%)Imbalance
Age (at enrolment) has 388 (18.2%) missing valuesMissing
original_record_index is uniformly distributedUniform
anonymous_patient_id has unique valuesUnique
Patient ID has unique valuesUnique
original_record_index has unique valuesUnique

Reproduction

Analysis started2025-11-11 11:16:07.230319
Analysis finished2025-11-11 11:19:55.521469
Duration3 minutes and 48.29 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct2129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size153.9 KiB
2025-11-11T13:19:55.932325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters36193
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2129 ?
Unique (%)100.0%

Sample

1st rowHEAT_226EDD0F846C
2nd rowHEAT_EE93F808E7B4
3rd rowHEAT_993C2108940A
4th rowHEAT_C93C89D0C6A0
5th rowHEAT_6354A5A00557
ValueCountFrequency (%)
heat_8a55ddbf10ea1
 
< 0.1%
heat_1c1730097ea61
 
< 0.1%
heat_226edd0f846c1
 
< 0.1%
heat_ee93f808e7b41
 
< 0.1%
heat_993c2108940a1
 
< 0.1%
heat_c93c89d0c6a01
 
< 0.1%
heat_6354a5a005571
 
< 0.1%
heat_2345aeca22421
 
< 0.1%
heat_05cbe5db49951
 
< 0.1%
heat_057ca59053651
 
< 0.1%
Other values (2119)2119
99.5%
2025-11-11T13:19:57.158345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A3750
 
10.4%
E3708
 
10.2%
H2129
 
5.9%
T2129
 
5.9%
_2129
 
5.9%
41652
 
4.6%
91631
 
4.5%
D1628
 
4.5%
21607
 
4.4%
01607
 
4.4%
Other values (9)14223
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)36193
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A3750
 
10.4%
E3708
 
10.2%
H2129
 
5.9%
T2129
 
5.9%
_2129
 
5.9%
41652
 
4.6%
91631
 
4.5%
D1628
 
4.5%
21607
 
4.4%
01607
 
4.4%
Other values (9)14223
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36193
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A3750
 
10.4%
E3708
 
10.2%
H2129
 
5.9%
T2129
 
5.9%
_2129
 
5.9%
41652
 
4.6%
91631
 
4.5%
D1628
 
4.5%
21607
 
4.4%
01607
 
4.4%
Other values (9)14223
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36193
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A3750
 
10.4%
E3708
 
10.2%
H2129
 
5.9%
T2129
 
5.9%
_2129
 
5.9%
41652
 
4.6%
91631
 
4.5%
D1628
 
4.5%
21607
 
4.4%
01607
 
4.4%
Other values (9)14223
39.3%

primary_date
Date

Primary date of measurement/visit

Distinct107
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
Minimum2020-06-15 00:00:00
Maximum2020-11-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T13:19:57.691734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:59.508807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

month
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7557539
Minimum6
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:00.653818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q17
median8
Q38
95-th percentile10
Maximum11
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0546603
Coefficient of variation (CV)0.13598424
Kurtosis0.89347083
Mean7.7557539
Median Absolute Deviation (MAD)1
Skewness0.86262371
Sum16512
Variance1.1123083
MonotonicityNot monotonic
2025-11-11T13:20:01.064584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8908
42.6%
7751
35.3%
10171
 
8.0%
6160
 
7.5%
9104
 
4.9%
1135
 
1.6%
ValueCountFrequency (%)
6160
 
7.5%
7751
35.3%
8908
42.6%
9104
 
4.9%
10171
 
8.0%
1135
 
1.6%
ValueCountFrequency (%)
1135
 
1.6%
10171
 
8.0%
9104
 
4.9%
8908
42.6%
7751
35.3%
6160
 
7.5%

season
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size131.0 KiB
Winter
1819 
Spring
310 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters12774
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Winter1819
85.4%
Spring310
 
14.6%

Length

2025-11-11T13:20:01.700238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T13:20:02.223840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
winter1819
85.4%
spring310
 
14.6%

Most occurring characters

ValueCountFrequency (%)
i2129
16.7%
r2129
16.7%
n2129
16.7%
W1819
14.2%
t1819
14.2%
e1819
14.2%
S310
 
2.4%
p310
 
2.4%
g310
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)12774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i2129
16.7%
r2129
16.7%
n2129
16.7%
W1819
14.2%
t1819
14.2%
e1819
14.2%
S310
 
2.4%
p310
 
2.4%
g310
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i2129
16.7%
r2129
16.7%
n2129
16.7%
W1819
14.2%
t1819
14.2%
e1819
14.2%
S310
 
2.4%
p310
 
2.4%
g310
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i2129
16.7%
r2129
16.7%
n2129
16.7%
W1819
14.2%
t1819
14.2%
e1819
14.2%
S310
 
2.4%
p310
 
2.4%
g310
 
2.4%

Age (at enrolment)
Real number (ℝ)

Missing 

Patient age at study enrollment

Distinct49
Distinct (%)2.8%
Missing388
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean33.873607
Minimum18
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:02.759389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q125
median32
Q341
95-th percentile56
Maximum65
Range47
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.307171
Coefficient of variation (CV)0.33380476
Kurtosis-0.44099388
Mean33.873607
Median Absolute Deviation (MAD)8
Skewness0.64950322
Sum58973.95
Variance127.85212
MonotonicityNot monotonic
2025-11-11T13:20:03.272410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2375
 
3.5%
2174
 
3.5%
2668
 
3.2%
2466
 
3.1%
2565
 
3.1%
3063
 
3.0%
3163
 
3.0%
2060
 
2.8%
1960
 
2.8%
2859
 
2.8%
Other values (39)1088
51.1%
(Missing)388
 
18.2%
ValueCountFrequency (%)
1840
1.9%
1960
2.8%
2060
2.8%
2174
3.5%
2258
2.7%
2375
3.5%
2466
3.1%
2565
3.1%
2668
3.2%
2754
2.5%
ValueCountFrequency (%)
652
 
0.1%
643
 
0.1%
639
0.4%
626
 
0.3%
6111
0.5%
606
 
0.3%
599
0.4%
5816
0.8%
5717
0.8%
5616
0.8%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size128.7 KiB
Male
1170 
Female
959 

Length

Max length6
Median length4
Mean length4.9008924
Min length4

Characters and Unicode

Total characters10434
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male1170
55.0%
Female959
45.0%

Length

2025-11-11T13:20:04.099423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T13:20:04.823868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male1170
55.0%
female959
45.0%

Most occurring characters

ValueCountFrequency (%)
e3088
29.6%
a2129
20.4%
l2129
20.4%
M1170
 
11.2%
F959
 
9.2%
m959
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e3088
29.6%
a2129
20.4%
l2129
20.4%
M1170
 
11.2%
F959
 
9.2%
m959
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e3088
29.6%
a2129
20.4%
l2129
20.4%
M1170
 
11.2%
F959
 
9.2%
m959
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e3088
29.6%
a2129
20.4%
l2129
20.4%
M1170
 
11.2%
F959
 
9.2%
m959
 
9.2%

Race
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size129.8 KiB
Black
1527 
Coloured
301 
White
260 
Other
 
37
Not reported
 
4

Length

Max length12
Median length5
Mean length5.4372945
Min length5

Characters and Unicode

Total characters11576
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlack
2nd rowBlack
3rd rowBlack
4th rowBlack
5th rowBlack

Common Values

ValueCountFrequency (%)
Black1527
71.7%
Coloured301
 
14.1%
White260
 
12.2%
Other37
 
1.7%
Not reported4
 
0.2%

Length

2025-11-11T13:20:05.570222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T13:20:06.336391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
black1527
71.6%
coloured301
 
14.1%
white260
 
12.2%
other37
 
1.7%
not4
 
0.2%
reported4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l1828
15.8%
B1527
13.2%
a1527
13.2%
c1527
13.2%
k1527
13.2%
o610
 
5.3%
e606
 
5.2%
r346
 
3.0%
d305
 
2.6%
t305
 
2.6%
Other values (9)1468
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)11576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l1828
15.8%
B1527
13.2%
a1527
13.2%
c1527
13.2%
k1527
13.2%
o610
 
5.3%
e606
 
5.2%
r346
 
3.0%
d305
 
2.6%
t305
 
2.6%
Other values (9)1468
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l1828
15.8%
B1527
13.2%
a1527
13.2%
c1527
13.2%
k1527
13.2%
o610
 
5.3%
e606
 
5.2%
r346
 
3.0%
d305
 
2.6%
t305
 
2.6%
Other values (9)1468
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l1828
15.8%
B1527
13.2%
a1527
13.2%
c1527
13.2%
k1527
13.2%
o610
 
5.3%
e606
 
5.2%
r346
 
3.0%
d305
 
2.6%
t305
 
2.6%
Other values (9)1468
12.7%

systolic blood pressure
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.88962
Minimum91
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:07.337776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile106
Q1119
median128
Q3136
95-th percentile150
Maximum196
Range105
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.355307
Coefficient of variation (CV)0.10442839
Kurtosis0.73567708
Mean127.88962
Median Absolute Deviation (MAD)9
Skewness0.37076619
Sum272277
Variance178.36422
MonotonicityNot monotonic
2025-11-11T13:20:08.008921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12875
 
3.5%
13875
 
3.5%
13575
 
3.5%
12973
 
3.4%
13072
 
3.4%
12766
 
3.1%
13665
 
3.1%
12664
 
3.0%
12464
 
3.0%
12561
 
2.9%
Other values (75)1439
67.6%
ValueCountFrequency (%)
911
 
< 0.1%
931
 
< 0.1%
941
 
< 0.1%
957
0.3%
962
 
0.1%
974
 
0.2%
994
 
0.2%
10011
0.5%
1019
0.4%
1027
0.3%
ValueCountFrequency (%)
1961
 
< 0.1%
1871
 
< 0.1%
1841
 
< 0.1%
1771
 
< 0.1%
1761
 
< 0.1%
1751
 
< 0.1%
1731
 
< 0.1%
1702
0.1%
1691
 
< 0.1%
1683
0.1%

diastolic blood pressure
Real number (ℝ)

High correlation 

Distinct65
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.705965
Minimum46
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:08.627701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile63
Q172
median79
Q385
95-th percentile95
Maximum122
Range76
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.7906165
Coefficient of variation (CV)0.12439485
Kurtosis0.5687087
Mean78.705965
Median Absolute Deviation (MAD)6
Skewness0.1218642
Sum167565
Variance95.856172
MonotonicityNot monotonic
2025-11-11T13:20:09.200884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76105
 
4.9%
7992
 
4.3%
7891
 
4.3%
7787
 
4.1%
8487
 
4.1%
8686
 
4.0%
8381
 
3.8%
8281
 
3.8%
7581
 
3.8%
8878
 
3.7%
Other values (55)1260
59.2%
ValueCountFrequency (%)
461
 
< 0.1%
491
 
< 0.1%
513
 
0.1%
523
 
0.1%
533
 
0.1%
546
0.3%
553
 
0.1%
566
0.3%
575
0.2%
5811
0.5%
ValueCountFrequency (%)
1221
 
< 0.1%
1191
 
< 0.1%
1181
 
< 0.1%
1162
0.1%
1123
0.1%
1093
0.1%
1072
0.1%
1062
0.1%
1052
0.1%
1042
0.1%

weight
Real number (ℝ)

High correlation 

Distinct582
Distinct (%)27.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean69.425423
Minimum34
Maximum128.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:09.842758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile49.7
Q158.475
median66.7
Q379.3
95-th percentile97.1
Maximum128.6
Range94.6
Interquartile range (IQR)20.825

Descriptive statistics

Standard deviation14.848056
Coefficient of variation (CV)0.21387059
Kurtosis0.16017414
Mean69.425423
Median Absolute Deviation (MAD)9.75
Skewness0.6826156
Sum147737.3
Variance220.46476
MonotonicityNot monotonic
2025-11-11T13:20:10.326299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6015
 
0.7%
6812
 
0.6%
64.712
 
0.6%
5611
 
0.5%
65.511
 
0.5%
66.111
 
0.5%
68.811
 
0.5%
7211
 
0.5%
79.911
 
0.5%
6511
 
0.5%
Other values (572)2012
94.5%
ValueCountFrequency (%)
341
< 0.1%
34.11
< 0.1%
35.51
< 0.1%
381
< 0.1%
38.52
0.1%
39.91
< 0.1%
402
0.1%
40.81
< 0.1%
41.91
< 0.1%
421
< 0.1%
ValueCountFrequency (%)
128.61
< 0.1%
121.61
< 0.1%
119.81
< 0.1%
1191
< 0.1%
1181
< 0.1%
117.71
< 0.1%
117.11
< 0.1%
1171
< 0.1%
116.51
< 0.1%
115.91
< 0.1%

Height
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.00423
Minimum130
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:10.806627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile152
Q1160
median167
Q3173
95-th percentile182.6
Maximum197
Range67
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2553826
Coefficient of variation (CV)0.05542005
Kurtosis-0.16970757
Mean167.00423
Median Absolute Deviation (MAD)6
Skewness0.051494303
Sum355552
Variance85.662106
MonotonicityNot monotonic
2025-11-11T13:20:11.346408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172108
 
5.1%
16592
 
4.3%
17090
 
4.2%
17188
 
4.1%
16685
 
4.0%
16085
 
4.0%
16284
 
3.9%
16384
 
3.9%
16984
 
3.9%
16879
 
3.7%
Other values (45)1250
58.7%
ValueCountFrequency (%)
1301
 
< 0.1%
1391
 
< 0.1%
1411
 
< 0.1%
1423
 
0.1%
1454
 
0.2%
1463
 
0.1%
1477
 
0.3%
14810
 
0.5%
14915
0.7%
15029
1.4%
ValueCountFrequency (%)
1971
 
< 0.1%
1951
 
< 0.1%
1931
 
< 0.1%
1924
 
0.2%
1918
0.4%
1904
 
0.2%
1895
 
0.2%
1889
0.4%
1877
0.3%
18615
0.7%

oral temperature
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)1.4%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean36.250659
Minimum34.1
Maximum37.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:11.912037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34.1
5-th percentile35.5
Q136
median36.2
Q336.5
95-th percentile36.9
Maximum37.5
Range3.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.41780045
Coefficient of variation (CV)0.01152532
Kurtosis0.95558618
Mean36.250659
Median Absolute Deviation (MAD)0.2
Skewness-0.34501116
Sum77068.9
Variance0.17455721
MonotonicityNot monotonic
2025-11-11T13:20:12.378756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
36317
14.9%
36.1248
11.6%
36.4210
9.9%
36.2187
8.8%
36.3181
8.5%
36.5178
8.4%
36.6122
 
5.7%
36.7115
 
5.4%
36.888
 
4.1%
35.980
 
3.8%
Other values (19)400
18.8%
ValueCountFrequency (%)
34.11
 
< 0.1%
34.61
 
< 0.1%
34.93
 
0.1%
3512
 
0.6%
35.119
0.9%
35.218
0.8%
35.314
 
0.7%
35.427
1.3%
35.526
1.2%
35.635
1.6%
ValueCountFrequency (%)
37.55
 
0.2%
37.42
 
0.1%
37.37
 
0.3%
37.211
 
0.5%
37.119
 
0.9%
3750
2.3%
36.955
2.6%
36.888
4.1%
36.7115
5.4%
36.6122
5.7%

Patient ID
Text

Unique 

Distinct2129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size128.9 KiB
2025-11-11T13:20:13.653206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters10645
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2129 ?
Unique (%)100.0%

Sample

1st rowCLHX9
2nd rowW52PQ
3rd rowAE247
4th row96LC8
5th rowUKPAU
ValueCountFrequency (%)
p4j8z1
 
< 0.1%
po0zx1
 
< 0.1%
clhx91
 
< 0.1%
w52pq1
 
< 0.1%
ae2471
 
< 0.1%
96lc81
 
< 0.1%
ukpau1
 
< 0.1%
muitn1
 
< 0.1%
fm3c41
 
< 0.1%
e7dwn1
 
< 0.1%
Other values (2119)2119
99.5%
2025-11-11T13:20:15.225808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P326
 
3.1%
1323
 
3.0%
4321
 
3.0%
I321
 
3.0%
2319
 
3.0%
G317
 
3.0%
L314
 
2.9%
U311
 
2.9%
A307
 
2.9%
5306
 
2.9%
Other values (26)7480
70.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P326
 
3.1%
1323
 
3.0%
4321
 
3.0%
I321
 
3.0%
2319
 
3.0%
G317
 
3.0%
L314
 
2.9%
U311
 
2.9%
A307
 
2.9%
5306
 
2.9%
Other values (26)7480
70.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P326
 
3.1%
1323
 
3.0%
4321
 
3.0%
I321
 
3.0%
2319
 
3.0%
G317
 
3.0%
L314
 
2.9%
U311
 
2.9%
A307
 
2.9%
5306
 
2.9%
Other values (26)7480
70.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P326
 
3.1%
1323
 
3.0%
4321
 
3.0%
I321
 
3.0%
2319
 
3.0%
G317
 
3.0%
L314
 
2.9%
U311
 
2.9%
A307
 
2.9%
5306
 
2.9%
Other values (26)7480
70.3%

original_record_index
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct2129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1064
Minimum0
Maximum2128
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:15.510716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile106.4
Q1532
median1064
Q31596
95-th percentile2021.6
Maximum2128
Range2128
Interquartile range (IQR)1064

Descriptive statistics

Standard deviation614.73368
Coefficient of variation (CV)0.57775722
Kurtosis-1.2
Mean1064
Median Absolute Deviation (MAD)532
Skewness0
Sum2265256
Variance377897.5
MonotonicityStrictly increasing
2025-11-11T13:20:16.065323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21281
 
< 0.1%
01
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
Other values (2119)2119
99.5%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
21281
< 0.1%
21271
< 0.1%
21261
< 0.1%
21251
< 0.1%
21241
< 0.1%
21231
< 0.1%
21221
< 0.1%
21211
< 0.1%
21201
< 0.1%
21191
< 0.1%

date
Date

Distinct107
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
Minimum2020-06-15 00:00:00
Maximum2020-11-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T13:20:16.945286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:20:18.721354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct107
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
Minimum2020-06-15 00:00:00
Maximum2020-11-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T13:20:20.400159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:20:22.444661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

HEAT_VULNERABILITY_SCORE
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.8 KiB
0.072879307465577
2126 
0.0
 
3

Length

Max length17
Median length17
Mean length16.980272
Min length3

Characters and Unicode

Total characters36151
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.072879307465577
2nd row0.072879307465577
3rd row0.072879307465577
4th row0.072879307465577
5th row0.072879307465577

Common Values

ValueCountFrequency (%)
0.0728793074655772126
99.9%
0.03
 
0.1%

Length

2025-11-11T13:20:24.042108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T13:20:24.654909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0728793074655772126
99.9%
0.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
710630
29.4%
06384
17.7%
54252
 
11.8%
.2129
 
5.9%
82126
 
5.9%
22126
 
5.9%
92126
 
5.9%
32126
 
5.9%
42126
 
5.9%
62126
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)36151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
710630
29.4%
06384
17.7%
54252
 
11.8%
.2129
 
5.9%
82126
 
5.9%
22126
 
5.9%
92126
 
5.9%
32126
 
5.9%
42126
 
5.9%
62126
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
710630
29.4%
06384
17.7%
54252
 
11.8%
.2129
 
5.9%
82126
 
5.9%
22126
 
5.9%
92126
 
5.9%
32126
 
5.9%
42126
 
5.9%
62126
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
710630
29.4%
06384
17.7%
54252
 
11.8%
.2129
 
5.9%
82126
 
5.9%
22126
 
5.9%
92126
 
5.9%
32126
 
5.9%
42126
 
5.9%
62126
 
5.9%

BMI (kg/m²)
Real number (ℝ)

High correlation 

Distinct247
Distinct (%)11.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean24.983788
Minimum14
Maximum40.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:25.341017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile18.035
Q120.7
median23.7
Q328.6
95-th percentile35.465
Maximum40.6
Range26.6
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation5.4382308
Coefficient of variation (CV)0.21767039
Kurtosis-0.26917661
Mean24.983788
Median Absolute Deviation (MAD)3.6
Skewness0.68683632
Sum53165.5
Variance29.574354
MonotonicityNot monotonic
2025-11-11T13:20:25.982617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.925
 
1.2%
21.525
 
1.2%
20.524
 
1.1%
20.824
 
1.1%
20.324
 
1.1%
22.223
 
1.1%
19.923
 
1.1%
20.722
 
1.0%
22.822
 
1.0%
22.421
 
1.0%
Other values (237)1895
89.0%
ValueCountFrequency (%)
141
 
< 0.1%
14.21
 
< 0.1%
14.71
 
< 0.1%
15.41
 
< 0.1%
15.51
 
< 0.1%
15.63
0.1%
15.71
 
< 0.1%
15.83
0.1%
15.91
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
40.61
 
< 0.1%
40.11
 
< 0.1%
39.91
 
< 0.1%
39.82
0.1%
39.61
 
< 0.1%
39.54
0.2%
39.42
0.1%
39.32
0.1%
39.23
0.1%
39.12
0.1%

Height (m)
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.00423
Minimum130
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:26.579292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile152
Q1160
median167
Q3173
95-th percentile182.6
Maximum197
Range67
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2553826
Coefficient of variation (CV)0.05542005
Kurtosis-0.16970757
Mean167.00423
Median Absolute Deviation (MAD)6
Skewness0.051494303
Sum355552
Variance85.662106
MonotonicityNot monotonic
2025-11-11T13:20:27.212296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172108
 
5.1%
16592
 
4.3%
17090
 
4.2%
17188
 
4.1%
16685
 
4.0%
16085
 
4.0%
16284
 
3.9%
16384
 
3.9%
16984
 
3.9%
16879
 
3.7%
Other values (45)1250
58.7%
ValueCountFrequency (%)
1301
 
< 0.1%
1391
 
< 0.1%
1411
 
< 0.1%
1423
 
0.1%
1454
 
0.2%
1463
 
0.1%
1477
 
0.3%
14810
 
0.5%
14915
0.7%
15029
1.4%
ValueCountFrequency (%)
1971
 
< 0.1%
1951
 
< 0.1%
1931
 
< 0.1%
1924
 
0.2%
1918
0.4%
1904
 
0.2%
1895
 
0.2%
1889
0.4%
1877
0.3%
18615
0.7%

Weight (kg)
Real number (ℝ)

High correlation 

Distinct582
Distinct (%)27.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean69.425423
Minimum34
Maximum128.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:27.909746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile49.7
Q158.475
median66.7
Q379.3
95-th percentile97.1
Maximum128.6
Range94.6
Interquartile range (IQR)20.825

Descriptive statistics

Standard deviation14.848056
Coefficient of variation (CV)0.21387059
Kurtosis0.16017414
Mean69.425423
Median Absolute Deviation (MAD)9.75
Skewness0.6826156
Sum147737.3
Variance220.46476
MonotonicityNot monotonic
2025-11-11T13:20:28.414276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6015
 
0.7%
6812
 
0.6%
64.712
 
0.6%
5611
 
0.5%
65.511
 
0.5%
66.111
 
0.5%
68.811
 
0.5%
7211
 
0.5%
79.911
 
0.5%
6511
 
0.5%
Other values (572)2012
94.5%
ValueCountFrequency (%)
341
< 0.1%
34.11
< 0.1%
35.51
< 0.1%
381
< 0.1%
38.52
0.1%
39.91
< 0.1%
402
0.1%
40.81
< 0.1%
41.91
< 0.1%
421
< 0.1%
ValueCountFrequency (%)
128.61
< 0.1%
121.61
< 0.1%
119.81
< 0.1%
1191
< 0.1%
1181
< 0.1%
117.71
< 0.1%
117.11
< 0.1%
1171
< 0.1%
116.51
< 0.1%
115.91
< 0.1%

Systolic blood pressure (mmHg)
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.88962
Minimum91
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:28.989140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile106
Q1119
median128
Q3136
95-th percentile150
Maximum196
Range105
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.355307
Coefficient of variation (CV)0.10442839
Kurtosis0.73567708
Mean127.88962
Median Absolute Deviation (MAD)9
Skewness0.37076619
Sum272277
Variance178.36422
MonotonicityNot monotonic
2025-11-11T13:20:29.654338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12875
 
3.5%
13875
 
3.5%
13575
 
3.5%
12973
 
3.4%
13072
 
3.4%
12766
 
3.1%
13665
 
3.1%
12664
 
3.0%
12464
 
3.0%
12561
 
2.9%
Other values (75)1439
67.6%
ValueCountFrequency (%)
911
 
< 0.1%
931
 
< 0.1%
941
 
< 0.1%
957
0.3%
962
 
0.1%
974
 
0.2%
994
 
0.2%
10011
0.5%
1019
0.4%
1027
0.3%
ValueCountFrequency (%)
1961
 
< 0.1%
1871
 
< 0.1%
1841
 
< 0.1%
1771
 
< 0.1%
1761
 
< 0.1%
1751
 
< 0.1%
1731
 
< 0.1%
1702
0.1%
1691
 
< 0.1%
1683
0.1%

Diastolic blood pressure (mmHg)
Real number (ℝ)

High correlation 

Distinct65
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.705965
Minimum46
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:30.301472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile63
Q172
median79
Q385
95-th percentile95
Maximum122
Range76
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.7906165
Coefficient of variation (CV)0.12439485
Kurtosis0.5687087
Mean78.705965
Median Absolute Deviation (MAD)6
Skewness0.1218642
Sum167565
Variance95.856172
MonotonicityNot monotonic
2025-11-11T13:20:30.868674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76105
 
4.9%
7992
 
4.3%
7891
 
4.3%
7787
 
4.1%
8487
 
4.1%
8686
 
4.0%
8381
 
3.8%
8281
 
3.8%
7581
 
3.8%
8878
 
3.7%
Other values (55)1260
59.2%
ValueCountFrequency (%)
461
 
< 0.1%
491
 
< 0.1%
513
 
0.1%
523
 
0.1%
533
 
0.1%
546
0.3%
553
 
0.1%
566
0.3%
575
0.2%
5811
0.5%
ValueCountFrequency (%)
1221
 
< 0.1%
1191
 
< 0.1%
1181
 
< 0.1%
1162
0.1%
1123
0.1%
1093
0.1%
1072
0.1%
1062
0.1%
1052
0.1%
1042
0.1%

Temperature (°C)
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)1.4%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean36.250659
Minimum34.1
Maximum37.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:31.477467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34.1
5-th percentile35.5
Q136
median36.2
Q336.5
95-th percentile36.9
Maximum37.5
Range3.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.41780045
Coefficient of variation (CV)0.01152532
Kurtosis0.95558618
Mean36.250659
Median Absolute Deviation (MAD)0.2
Skewness-0.34501116
Sum77068.9
Variance0.17455721
MonotonicityNot monotonic
2025-11-11T13:20:32.029020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
36317
14.9%
36.1248
11.6%
36.4210
9.9%
36.2187
8.8%
36.3181
8.5%
36.5178
8.4%
36.6122
 
5.7%
36.7115
 
5.4%
36.888
 
4.1%
35.980
 
3.8%
Other values (19)400
18.8%
ValueCountFrequency (%)
34.11
 
< 0.1%
34.61
 
< 0.1%
34.93
 
0.1%
3512
 
0.6%
35.119
0.9%
35.218
0.8%
35.314
 
0.7%
35.427
1.3%
35.526
1.2%
35.635
1.6%
ValueCountFrequency (%)
37.55
 
0.2%
37.42
 
0.1%
37.37
 
0.3%
37.211
 
0.5%
37.119
 
0.9%
3750
2.3%
36.955
2.6%
36.888
4.1%
36.7115
5.4%
36.6122
5.7%

Heart rate (bpm)
Real number (ℝ)

Distinct67
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.496477
Minimum43
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:32.716816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile55
Q164
median72
Q381
95-th percentile92
Maximum120
Range77
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.633223
Coefficient of variation (CV)0.16046604
Kurtosis-0.27976066
Mean72.496477
Median Absolute Deviation (MAD)9
Skewness0.26029605
Sum154345
Variance135.33187
MonotonicityNot monotonic
2025-11-11T13:20:33.278311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6091
 
4.3%
7679
 
3.7%
6478
 
3.7%
7874
 
3.5%
8469
 
3.2%
6769
 
3.2%
6869
 
3.2%
7268
 
3.2%
6268
 
3.2%
8067
 
3.1%
Other values (57)1397
65.6%
ValueCountFrequency (%)
432
 
0.1%
441
 
< 0.1%
454
 
0.2%
461
 
< 0.1%
475
 
0.2%
488
0.4%
5013
0.6%
5114
0.7%
5217
0.8%
5312
0.6%
ValueCountFrequency (%)
1201
 
< 0.1%
1141
 
< 0.1%
1101
 
< 0.1%
1091
 
< 0.1%
1081
 
< 0.1%
1071
 
< 0.1%
1051
 
< 0.1%
1031
 
< 0.1%
1021
 
< 0.1%
1013
0.1%

Respiratory rate (breaths/min)
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)0.7%
Missing6
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean17.851625
Minimum11
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:33.869721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile14
Q117
median18
Q319
95-th percentile21
Maximum52
Range41
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2157714
Coefficient of variation (CV)0.12412155
Kurtosis26.475629
Mean17.851625
Median Absolute Deviation (MAD)1
Skewness1.3332051
Sum37899
Variance4.9096427
MonotonicityNot monotonic
2025-11-11T13:20:34.296198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
18786
36.9%
20364
17.1%
16285
 
13.4%
19180
 
8.5%
17146
 
6.9%
1497
 
4.6%
2160
 
2.8%
1555
 
2.6%
1349
 
2.3%
2242
 
2.0%
Other values (5)59
 
2.8%
ValueCountFrequency (%)
111
 
< 0.1%
1234
 
1.6%
1349
 
2.3%
1497
 
4.6%
1555
 
2.6%
16285
 
13.4%
17146
 
6.9%
18786
36.9%
19180
 
8.5%
20364
17.1%
ValueCountFrequency (%)
521
 
< 0.1%
245
 
0.2%
2318
 
0.8%
2242
 
2.0%
2160
 
2.8%
20364
17.1%
19180
 
8.5%
18786
36.9%
17146
 
6.9%
16285
 
13.4%

Oxygen saturation (%)
Real number (ℝ)

Distinct12
Distinct (%)0.6%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean97.992948
Minimum89
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2025-11-11T13:20:34.819966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile96
Q197
median98
Q399
95-th percentile100
Maximum100
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3614941
Coefficient of variation (CV)0.013893797
Kurtosis2.3312733
Mean97.992948
Median Absolute Deviation (MAD)1
Skewness-0.98678555
Sum208431
Variance1.8536661
MonotonicityNot monotonic
2025-11-11T13:20:35.362670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
99676
31.8%
98581
27.3%
97358
16.8%
96216
 
10.1%
100206
 
9.7%
9572
 
3.4%
948
 
0.4%
934
 
0.2%
902
 
0.1%
912
 
0.1%
Other values (2)2
 
0.1%
(Missing)2
 
0.1%
ValueCountFrequency (%)
891
 
< 0.1%
902
 
0.1%
912
 
0.1%
921
 
< 0.1%
934
 
0.2%
948
 
0.4%
9572
 
3.4%
96216
 
10.1%
97358
16.8%
98581
27.3%
ValueCountFrequency (%)
100206
 
9.7%
99676
31.8%
98581
27.3%
97358
16.8%
96216
 
10.1%
9572
 
3.4%
948
 
0.4%
934
 
0.2%
921
 
< 0.1%
912
 
0.1%

Interactions

2025-11-11T13:19:31.541148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:14.550060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:24.281330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:36.078984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:48.657471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:02.610026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:13.071729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:25.502245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:40.060699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:52.780146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:03.286758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:15.313806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:25.655160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:38.827299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:52.999411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:06.022111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:18.664661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:32.085527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:14.856998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:24.811180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:36.691958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:49.358941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:03.098349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:13.665411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:26.230287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:40.601354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:53.266215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:03.863076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:15.802878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:26.302197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:39.551403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:53.644024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:06.777109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:19.297345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:32.766775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:15.379815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:25.319952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:37.382960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:50.129390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:03.665631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:14.336214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T13:18:04.514374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:16.352428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:27.032678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:40.391098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:54.395844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:07.410636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:20.099895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:33.575357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:15.972121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:26.038729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:38.042064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:50.988722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:04.316064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:15.091786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:27.952912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:42.091209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:54.447292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:05.216186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:16.998372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:27.760690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:41.302591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:55.209009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:08.137515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:20.945004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:34.390721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:16.611804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:26.827477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:38.811788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T13:17:28.900909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:42.855167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:55.129152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:06.035623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:17.658782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T13:16:27.411258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:39.425603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:52.443152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:05.438801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:16.503226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:29.656323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:43.465063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:55.622509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:06.692749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:18.151476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:29.147577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:42.931950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:56.734442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:09.410960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:22.478953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:35.770223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:17.607227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:28.121786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:40.137903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:53.274206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:06.067817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:17.109370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:30.523301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T13:17:56.231289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T13:18:29.843656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:43.809243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:57.512330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:10.140121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:23.240844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:36.594505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:18.279548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:28.945063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:40.985051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:54.231269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:06.810878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:17.959678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:31.333027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:44.962241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:56.948186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:08.102898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:19.470749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:30.698063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:44.804424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:58.283974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:10.963480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:24.136770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:37.389217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:18.843752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:29.800285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:41.743216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:55.057568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:07.391632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:18.716920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:32.226881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:45.569127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:57.591980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:08.836868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:20.113930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:31.575995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:45.700960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:59.012244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:11.721486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:24.924935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:38.076057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:19.333369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:30.580361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:42.427827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:55.818758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:07.908961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:19.374616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:33.016253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:46.259960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:58.056386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:09.472586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:20.665683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:32.248370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:46.502907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:59.720279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:12.446510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:25.613247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:38.816197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:19.935485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:31.244860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:43.155730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:56.667635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:08.488361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:20.038350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:33.908284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:47.053973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:58.662738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:10.102063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:21.303472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:32.975954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:47.407745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:00.496661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:13.176763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:26.387177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:39.477277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:20.429769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:31.805040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:43.821364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:57.386521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:08.983007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:20.685230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:34.708559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:47.756056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:59.202247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:10.729967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:21.767882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:33.634555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:48.154573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:01.206882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:13.805362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:27.083522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:40.291341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:21.087377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:32.535031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:44.574255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:58.277979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:09.689819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:21.470340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:35.668530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:48.648337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:59.922382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:11.478404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:22.422086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:34.403621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:49.026163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:02.054208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:14.613915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:27.915963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:41.172676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:21.766283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:33.276625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:45.379011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:59.169239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:10.420337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:22.329080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:36.663161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:49.822518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:00.669622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:12.287765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:23.058153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:35.335474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:49.761128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:02.932025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:15.460377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:28.719925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:42.067418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:22.436261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:34.042087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:46.212874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:00.148491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:11.187041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:23.194043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:37.533533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:50.640015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:01.412223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:13.103650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:23.783772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:36.284470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:50.666140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:03.645407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:16.381469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:29.518365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:42.869284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:23.078504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:34.708493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:46.997169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:00.948425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:11.817572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:23.983404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:38.445680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:51.387084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:02.055958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:13.864334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:24.401522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:37.145163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:51.427983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:04.445798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:17.081856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:30.247732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:43.656144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:23.715423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:35.436835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:16:47.866941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:01.782898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:12.471658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:24.745597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:39.251641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:17:52.119890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:02.678714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:14.590803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:25.044317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:38.013177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:18:52.235446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:05.236730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:17.912167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:19:30.848529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-11T13:20:35.880182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
monthAge (at enrolment)systolic blood pressurediastolic blood pressureweightHeightoral temperatureoriginal_record_indexHEAT_VULNERABILITY_SCOREBMI (kg/m²)Height (m)Weight (kg)Systolic blood pressure (mmHg)Diastolic blood pressure (mmHg)Temperature (°C)Heart rate (bpm)Respiratory rate (breaths/min)Oxygen saturation (%)
month1.0000.0140.005-0.0330.003-0.0930.1070.3070.0150.049-0.0930.0030.005-0.0330.1070.087-0.1380.124
Age (at enrolment)0.0141.0000.3130.2440.2220.022-0.1450.003-0.0460.2070.0220.2220.3130.244-0.145-0.0330.130-0.106
systolic blood pressure0.0050.3131.0000.6180.2210.077-0.037-0.014-0.0440.1790.0770.2211.0000.618-0.0370.0270.0370.029
diastolic blood pressure-0.0330.2440.6181.0000.1960.050-0.0080.034-0.0100.1690.0500.1960.6181.000-0.0080.1070.0730.016
weight0.0030.2220.2210.1961.0000.2460.005-0.021-0.0070.8540.2461.0000.2210.1960.0050.0790.090-0.054
Height-0.0930.0220.0770.0500.2461.000-0.089-0.1190.041-0.2831.0000.2460.0770.050-0.089-0.2320.055-0.068
oral temperature0.107-0.145-0.037-0.0080.005-0.0891.0000.228NaN0.054-0.0890.005-0.037-0.0081.0000.147-0.050-0.024
original_record_index0.3070.003-0.0140.034-0.021-0.1190.2281.0000.0480.035-0.119-0.021-0.0140.0340.2280.101-0.1640.108
HEAT_VULNERABILITY_SCORE0.015-0.046-0.044-0.010-0.0070.041NaN0.0481.000-0.0300.041-0.007-0.044-0.010NaN0.0020.0310.018
BMI (kg/m²)0.0490.2070.1790.1690.854-0.2830.0540.035-0.0301.000-0.2830.8540.1790.1690.0540.2030.057-0.016
Height (m)-0.0930.0220.0770.0500.2461.000-0.089-0.1190.041-0.2831.0000.2460.0770.050-0.089-0.2320.055-0.068
Weight (kg)0.0030.2220.2210.1961.0000.2460.005-0.021-0.0070.8540.2461.0000.2210.1960.0050.0790.090-0.054
Systolic blood pressure (mmHg)0.0050.3131.0000.6180.2210.077-0.037-0.014-0.0440.1790.0770.2211.0000.618-0.0370.0270.0370.029
Diastolic blood pressure (mmHg)-0.0330.2440.6181.0000.1960.050-0.0080.034-0.0100.1690.0500.1960.6181.000-0.0080.1070.0730.016
Temperature (°C)0.107-0.145-0.037-0.0080.005-0.0891.0000.228NaN0.054-0.0890.005-0.037-0.0081.0000.147-0.050-0.024
Heart rate (bpm)0.087-0.0330.0270.1070.079-0.2320.1470.1010.0020.203-0.2320.0790.0270.1070.1471.0000.019-0.049
Respiratory rate (breaths/min)-0.1380.1300.0370.0730.0900.055-0.050-0.1640.0310.0570.0550.0900.0370.073-0.0500.0191.0000.028
Oxygen saturation (%)0.124-0.1060.0290.016-0.054-0.068-0.0240.1080.018-0.016-0.068-0.0540.0290.016-0.024-0.0490.0281.000
2025-11-11T13:20:40.110202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
monthAge (at enrolment)systolic blood pressurediastolic blood pressureweightHeightoral temperatureoriginal_record_indexHEAT_VULNERABILITY_SCOREBMI (kg/m²)Height (m)Weight (kg)Systolic blood pressure (mmHg)Diastolic blood pressure (mmHg)Temperature (°C)Heart rate (bpm)Respiratory rate (breaths/min)Oxygen saturation (%)
month1.0000.0130.011-0.0100.031-0.0910.1280.4050.0160.069-0.0910.0310.011-0.0100.1280.079-0.1640.132
Age (at enrolment)0.0131.0000.2750.2430.2390.015-0.142-0.009-0.0420.2310.0150.2390.2750.243-0.142-0.0320.151-0.104
systolic blood pressure0.0110.2751.0000.6050.2330.084-0.047-0.028-0.0350.1900.0840.2331.0000.605-0.0470.0320.037-0.001
diastolic blood pressure-0.0100.2430.6051.0000.1950.051-0.0110.027-0.0120.1690.0510.1950.6051.000-0.0110.1020.072-0.013
weight0.0310.2390.2330.1951.0000.1930.012-0.025-0.0110.8670.1931.0000.2330.1950.0120.0780.080-0.062
Height-0.0910.0150.0840.0510.1931.000-0.086-0.1240.043-0.2891.0000.1930.0840.051-0.086-0.2410.056-0.083
oral temperature0.128-0.142-0.047-0.0110.012-0.0861.0000.234NaN0.051-0.0860.012-0.047-0.0111.0000.152-0.064-0.014
original_record_index0.405-0.009-0.0280.027-0.025-0.1240.2341.0000.0480.037-0.124-0.025-0.0280.0270.2340.109-0.1470.148
HEAT_VULNERABILITY_SCORE0.016-0.042-0.035-0.012-0.0110.043NaN0.0481.000-0.0320.043-0.011-0.035-0.012NaN-0.0000.0340.027
BMI (kg/m²)0.0690.2310.1900.1690.867-0.2890.0510.037-0.0321.000-0.2890.8670.1900.1690.0510.1900.052-0.031
Height (m)-0.0910.0150.0840.0510.1931.000-0.086-0.1240.043-0.2891.0000.1930.0840.051-0.086-0.2410.056-0.083
Weight (kg)0.0310.2390.2330.1951.0000.1930.012-0.025-0.0110.8670.1931.0000.2330.1950.0120.0780.080-0.062
Systolic blood pressure (mmHg)0.0110.2751.0000.6050.2330.084-0.047-0.028-0.0350.1900.0840.2331.0000.605-0.0470.0320.037-0.001
Diastolic blood pressure (mmHg)-0.0100.2430.6051.0000.1950.051-0.0110.027-0.0120.1690.0510.1950.6051.000-0.0110.1020.072-0.013
Temperature (°C)0.128-0.142-0.047-0.0110.012-0.0861.0000.234NaN0.051-0.0860.012-0.047-0.0111.0000.152-0.064-0.014
Heart rate (bpm)0.079-0.0320.0320.1020.078-0.2410.1520.109-0.0000.190-0.2410.0780.0320.1020.1521.0000.000-0.045
Respiratory rate (breaths/min)-0.1640.1510.0370.0720.0800.056-0.064-0.1470.0340.0520.0560.0800.0370.072-0.0640.0001.0000.027
Oxygen saturation (%)0.132-0.104-0.001-0.013-0.062-0.083-0.0140.1480.027-0.031-0.083-0.062-0.001-0.013-0.014-0.0450.0271.000
2025-11-11T13:20:44.350835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
monthseasonAge (at enrolment)SexRacesystolic blood pressurediastolic blood pressureweightHeightoral temperatureoriginal_record_indexHEAT_VULNERABILITY_SCOREBMI (kg/m²)Height (m)Weight (kg)Systolic blood pressure (mmHg)Diastolic blood pressure (mmHg)Temperature (°C)Heart rate (bpm)Respiratory rate (breaths/min)Oxygen saturation (%)
month1.0001.0000.0640.1970.2560.0850.0940.0530.0760.1600.6950.0000.0440.0760.0530.0850.0940.1600.1140.1370.132
season1.0001.0000.0000.1800.1350.0870.1330.0000.1120.1420.7430.0000.0930.1120.0000.0870.1330.1420.0770.0770.117
Age (at enrolment)0.0640.0001.0000.1280.4200.3670.3010.2740.0000.2080.2080.0800.2610.0000.2740.3670.3010.2080.0650.2450.120
Sex0.1970.1800.1281.0000.1140.1510.0000.2770.7700.0750.2160.0000.5620.7700.2770.1510.0000.0750.4090.0210.150
Race0.2560.1350.4200.1141.0000.1470.1800.2810.3090.1550.7290.0000.1810.3090.2810.1470.1800.1550.1880.1960.240
systolic blood pressure0.0850.0870.3670.1510.1471.0000.7080.2200.1150.1110.2150.1880.2150.1150.2201.0000.7080.1110.1000.1100.176
diastolic blood pressure0.0940.1330.3010.0000.1800.7081.0000.2020.0430.0000.2090.0000.1830.0430.2020.7081.0000.0000.2220.0780.304
weight0.0530.0000.2740.2770.2810.2200.2021.0000.4860.0000.1740.0000.8120.4861.0000.2200.2020.0000.1530.0790.000
Height0.0760.1120.0000.7700.3090.1150.0430.4861.0000.1070.2380.0000.3461.0000.4860.1150.0430.1070.2360.0000.078
oral temperature0.1600.1420.2080.0750.1550.1110.0000.0000.1071.0000.349NaN0.1370.1070.0000.1110.0001.0000.1700.1050.000
original_record_index0.6950.7430.2080.2160.7290.2150.2090.1740.2380.3491.0000.1200.0950.2380.1740.2150.2090.3490.3220.5080.356
HEAT_VULNERABILITY_SCORE0.0000.0000.0800.0000.0000.1880.0000.0000.000NaN0.1201.0000.0000.0000.0000.1880.000NaN0.0000.0000.000
BMI (kg/m²)0.0440.0930.2610.5620.1810.2150.1830.8120.3460.1370.0950.0001.0000.3460.8120.2150.1830.1370.2150.0000.000
Height (m)0.0760.1120.0000.7700.3090.1150.0430.4861.0000.1070.2380.0000.3461.0000.4860.1150.0430.1070.2360.0000.078
Weight (kg)0.0530.0000.2740.2770.2810.2200.2021.0000.4860.0000.1740.0000.8120.4861.0000.2200.2020.0000.1530.0790.000
Systolic blood pressure (mmHg)0.0850.0870.3670.1510.1471.0000.7080.2200.1150.1110.2150.1880.2150.1150.2201.0000.7080.1110.1000.1100.176
Diastolic blood pressure (mmHg)0.0940.1330.3010.0000.1800.7081.0000.2020.0430.0000.2090.0000.1830.0430.2020.7081.0000.0000.2220.0780.304
Temperature (°C)0.1600.1420.2080.0750.1550.1110.0000.0000.1071.0000.349NaN0.1370.1070.0000.1110.0001.0000.1700.1050.000
Heart rate (bpm)0.1140.0770.0650.4090.1880.1000.2220.1530.2360.1700.3220.0000.2150.2360.1530.1000.2220.1701.0000.3610.080
Respiratory rate (breaths/min)0.1370.0770.2450.0210.1960.1100.0780.0790.0000.1050.5080.0000.0000.0000.0790.1100.0780.1050.3611.0000.000
Oxygen saturation (%)0.1320.1170.1200.1500.2400.1760.3040.0000.0780.0000.3560.0000.0000.0780.0000.1760.3040.0000.0800.0001.000
2025-11-11T13:20:48.473821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
HEAT_VULNERABILITY_SCORERaceSexseason
HEAT_VULNERABILITY_SCORE1.0000.0000.0000.000
Race0.0001.0000.1390.165
Sex0.0000.1391.0000.115
season0.0000.1650.1151.000
2025-11-11T13:20:49.327891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)Diastolic blood pressure (mmHg)HEAT_VULNERABILITY_SCOREHeart rate (bpm)HeightHeight (m)Oxygen saturation (%)RaceRespiratory rate (breaths/min)SexSystolic blood pressure (mmHg)Temperature (°C)Weight (kg)diastolic blood pressuremonthoral temperatureoriginal_record_indexseasonsystolic blood pressureweight
Age (at enrolment)1.0000.2310.2430.061-0.0320.0150.015-0.1040.1870.1510.0980.275-0.1420.2390.2430.013-0.142-0.0090.0000.2750.239
BMI (kg/m²)0.2311.0000.1690.0000.190-0.289-0.289-0.0310.0760.0520.4340.1900.0510.8670.1690.0690.0510.0370.0720.1900.867
Diastolic blood pressure (mmHg)0.2430.1691.0000.0000.1020.0510.051-0.0130.0730.0720.0000.605-0.0110.1951.000-0.010-0.0110.0270.1030.6050.195
HEAT_VULNERABILITY_SCORE0.0610.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.1361.0000.0000.0000.0001.0000.0920.0000.1360.000
Heart rate (bpm)-0.0320.1900.1020.0001.000-0.241-0.241-0.0450.0790.0000.3140.0320.1520.0780.1020.0790.1520.1090.0590.0320.078
Height0.015-0.2890.0510.000-0.2411.0001.000-0.0830.1330.0560.6050.084-0.0860.1930.051-0.091-0.086-0.1240.0860.0840.193
Height (m)0.015-0.2890.0510.000-0.2411.0001.000-0.0830.1330.0560.6050.084-0.0860.1930.051-0.091-0.086-0.1240.0860.0840.193
Oxygen saturation (%)-0.104-0.031-0.0130.000-0.045-0.083-0.0831.0000.1020.0270.115-0.001-0.014-0.062-0.0130.132-0.0140.1480.090-0.001-0.062
Race0.1870.0760.0730.0000.0790.1330.1330.1021.0000.0740.1390.0610.0780.1200.0730.1760.0780.3880.1650.0610.120
Respiratory rate (breaths/min)0.1510.0520.0720.0000.0000.0560.0560.0270.0741.0000.0260.037-0.0640.0800.072-0.164-0.064-0.1470.0940.0370.080
Sex0.0980.4340.0000.0000.3140.6050.6050.1150.1390.0261.0000.1110.0560.2120.0000.1420.0560.1650.1150.1110.212
Systolic blood pressure (mmHg)0.2750.1900.6050.1360.0320.0840.084-0.0010.0610.0370.1111.000-0.0470.2330.6050.011-0.047-0.0280.0661.0000.233
Temperature (°C)-0.1420.051-0.0111.0000.152-0.086-0.086-0.0140.078-0.0640.056-0.0471.0000.012-0.0110.1281.0000.2340.105-0.0470.012
Weight (kg)0.2390.8670.1950.0000.0780.1930.193-0.0620.1200.0800.2120.2330.0121.0000.1950.0310.012-0.0250.0000.2331.000
diastolic blood pressure0.2430.1691.0000.0000.1020.0510.051-0.0130.0730.0720.0000.605-0.0110.1951.000-0.010-0.0110.0270.1030.6050.195
month0.0130.069-0.0100.0000.079-0.091-0.0910.1320.176-0.1640.1420.0110.1280.031-0.0101.0000.1280.4050.9990.0110.031
oral temperature-0.1420.051-0.0111.0000.152-0.086-0.086-0.0140.078-0.0640.056-0.0471.0000.012-0.0110.1281.0000.2340.105-0.0470.012
original_record_index-0.0090.0370.0270.0920.109-0.124-0.1240.1480.388-0.1470.165-0.0280.234-0.0250.0270.4050.2341.0000.582-0.028-0.025
season0.0000.0720.1030.0000.0590.0860.0860.0900.1650.0940.1150.0660.1050.0000.1030.9990.1050.5821.0000.0660.000
systolic blood pressure0.2750.1900.6050.1360.0320.0840.084-0.0010.0610.0370.1111.000-0.0470.2330.6050.011-0.047-0.0280.0661.0000.233
weight0.2390.8670.1950.0000.0780.1930.193-0.0620.1200.0800.2120.2330.0121.0000.1950.0310.012-0.0250.0000.2331.000

Missing values

2025-11-11T13:19:44.442356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-11T13:19:48.778503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-11T13:19:53.542996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

anonymous_patient_idprimary_datemonthseasonAge (at enrolment)SexRacesystolic blood pressurediastolic blood pressureweightHeightoral temperaturePatient IDoriginal_record_indexdateprimary_date_parsedHEAT_VULNERABILITY_SCOREBMI (kg/m²)Height (m)Weight (kg)Systolic blood pressure (mmHg)Diastolic blood pressure (mmHg)Temperature (°C)Heart rate (bpm)Respiratory rate (breaths/min)Oxygen saturation (%)
5357HEAT_226EDD0F846C2020-06-186Winter55.0FemaleBlack130.079.082.4159.035.7CLHX90.02020-06-182020-06-180.07287932.6159.082.4130.079.035.769.024.099.0
5358HEAT_EE93F808E7B42020-06-186Winter29.0MaleBlack133.084.069.9172.035.7W52PQ1.02020-06-182020-06-180.07287923.6172.069.9133.084.035.764.020.099.0
5359HEAT_993C2108940A2020-06-186Winter31.0MaleBlack126.075.047.1160.036.3AE2472.02020-06-182020-06-180.07287918.4160.047.1126.075.036.357.020.099.0
5360HEAT_C93C89D0C6A02020-06-196Winter32.0MaleBlack125.076.048.6163.036.096LC83.02020-06-192020-06-190.07287918.3163.048.6125.076.036.069.020.099.0
5361HEAT_6354A5A005572020-06-196Winter29.0MaleBlack120.076.069.0160.035.8UKPAU4.02020-06-192020-06-190.07287927.0160.069.0120.076.035.860.018.096.0
5362HEAT_2345AECA22422020-06-196Winter24.0MaleBlack147.075.074.8175.035.9MUITN5.02020-06-192020-06-190.07287924.4175.074.8147.075.035.971.019.0100.0
5363HEAT_05CBE5DB49952020-06-196Winter46.0FemaleBlack125.080.085.1157.036.4FM3C46.02020-06-192020-06-190.07287934.5157.085.1125.080.036.474.018.098.0
5364HEAT_057CA59053652020-06-196Winter21.0FemaleBlack114.081.047.0159.036.1E7DWN7.02020-06-192020-06-190.07287918.6159.047.0114.081.036.193.020.099.0
5365HEAT_3DC00A95835C2020-06-196Winter23.0MaleBlack117.077.063.2178.036.0M3PUL8.02020-06-192020-06-190.07287919.9178.063.2117.077.036.052.020.098.0
5366HEAT_80CAE44FC0D62020-06-196Winter31.0MaleBlack133.077.072.2165.035.69BWOB9.02020-06-192020-06-190.07287926.5165.072.2133.077.035.668.020.0100.0
anonymous_patient_idprimary_datemonthseasonAge (at enrolment)SexRacesystolic blood pressurediastolic blood pressureweightHeightoral temperaturePatient IDoriginal_record_indexdateprimary_date_parsedHEAT_VULNERABILITY_SCOREBMI (kg/m²)Height (m)Weight (kg)Systolic blood pressure (mmHg)Diastolic blood pressure (mmHg)Temperature (°C)Heart rate (bpm)Respiratory rate (breaths/min)Oxygen saturation (%)
7476HEAT_7A22C7AB94C82020-08-298WinterNaNMaleBlack126.081.061.7184.035.6T8C7I2119.02020-08-292020-08-290.07287918.2184.061.7126.081.035.670.014.097.0
7477HEAT_E8787EB964D92020-08-298WinterNaNMaleBlack132.079.056.2163.036.9SGDWV2120.02020-08-292020-08-290.07287921.2163.056.2132.079.036.977.014.098.0
7478HEAT_43908FF64F762020-08-298WinterNaNMaleBlack127.064.055.4165.036.7R5B122121.02020-08-292020-08-290.07287920.3165.055.4127.064.036.757.013.099.0
7479HEAT_E4BC92F9E2AA2020-08-298WinterNaNMaleBlack109.065.064.6165.036.5OVIVG2122.02020-08-292020-08-290.07287923.7165.064.6109.065.036.562.012.097.0
7480HEAT_35BC4F9D14E52020-08-298WinterNaNMaleBlack127.089.064.5170.036.2IMOMT2123.02020-08-292020-08-290.07287922.3170.064.5127.089.036.297.015.099.0
7481HEAT_51623202CDCF2020-08-298WinterNaNFemaleBlack131.086.066.1154.036.11NDSB2124.02020-08-292020-08-290.07287927.9154.066.1131.086.036.185.013.097.0
7482HEAT_BA5802A10F2D2020-08-298WinterNaNFemaleBlack129.090.073.3155.036.1G3TTH2125.02020-08-292020-08-290.07287930.5155.073.3129.090.036.177.014.098.0
7483HEAT_6AE99C6061A32020-08-298WinterNaNMaleBlack140.086.053.0166.036.3FOHHD2126.02020-08-292020-08-290.07287919.2166.053.0140.086.036.360.015.099.0
7484HEAT_CE6CB1521F5F2020-08-298WinterNaNMaleBlack124.071.058.4168.036.2M6KPZ2127.02020-08-292020-08-290.07287920.7168.058.4124.071.036.250.014.099.0
7485HEAT_1C1730097EA62020-08-298WinterNaNMaleBlack118.070.051.5157.036.2PO0ZX2128.02020-08-292020-08-290.07287920.9157.051.5118.070.036.256.016.099.0